Cyclostationary Detection Based Spectrum Sensing for Cognitive Radio Networks

Abstract-In this paper, cyclostationary detection Based spectrum sensing is considered for cognitive radio networks. We first summarize the existing first-order and second-order cyclostationary detection algorithms, which can be considered as a brief tutorial on detection theory of the cyclostationary signals. Based on this, we propose a cooperative spectrum sensing method for a cognitive radio networks with multiple terminals and one fusion center. It is shown that the proposed method have reliable performance even in low signal-to-noise ratio (SNR) region. It is also found that the increasing number of secondary users (SUs) can result in improved detection performance, especially at low SNR. Simulation results are then provided to corroborate the proposed studies. Index Terms-Cognitive radio, cooperative spectrum sensing, cyclostationary detection

Energy Detector Based Spectrum Sensing Performance Analysis over Fading Environment

Cognitive radio is a new concept of wireless communication that offers increased usage of the limited spectral resource and is considered to be a revolutionary technology that will influence how radio spectrum is accessed, accessed and controlled in the future. Spectrum sensing is needed to allow optimal use of spectral resource. Secondary user performs spectrum sensing to recognize the transmission possibilities. Secondary users have lower priority when using spectrum, so a basic principle is that secondary users should avoid / minimize interference with primary users. We seek to identify the transmission from primary users for the spectrum sensing. Detection of the primary transmitter assists in the recognition of the spectrum it uses. Utilizing spectrum sensing approach, secondary user starts communication if it detects a weak signal or white space. Because of multipath propagation and shadowing effects, primary transmitter's detection is severely influenced. There are numerous spectrum sensing mechanisms and one of them is energy detection approach.  In this paper, we have examined the impact of SNR on probability of detection in order to assess the performance of spectrum sensing using energy detector. Also the receiver operating characteristic curve was plotted for performance analysis of spectrum sensing employing energy detector. In addition, we also examined the impact of threshold value on the probability of the false alarm

Energy Detector Based Spectrum Sensing Performance Analysis over Fading Environment

239-244Energy detection approach for sensing of spectrum is an extremely effective method of detection in comparison of other spectrum sensing methods when secondary user lacks adequate knowledge of primary user's channel conditions. Because of multipath propagation and shadowing effects, performance of energy detector employed in a cognitive radio system is severely influenced. In this paper, we have evaluated performance of energy detector over fading environment. Hypothesis testing was utilized for spectrum sensing to find out whether the primary user's signal was available or missing. Performance assessment for spectrum sensing using the energy detector was carried out primarily on the basis of probability of false alarm and probability of detection. We have examined the impact of SNR on probability of detection in order to assess the performance of spectrum sensing using energy detector. Also the receiver operating characteristic curve was plotted for performance analysis of spectrum sensing employing energy detector. In addition, we also examined the impact of threshold value on the probability of the false alarm. We have found that probability of detection improves when we increase the value of signal to noise ratio and use more number of samples. We have also observed that false alarm probability decreases when we increase the threshold value

LMPIT-inspired Tests for Detecting a Cyclostationary Signal in Noise with Spatio-Temporal Structure

In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios; here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework

A Cooperative Bayesian Nonparametric Framework for Primary User Activity Monitoring in Cognitive Radio Network

This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users(PUs), to cooperate and use \emph{Bayesian nonparametric} techniques to estimate the distributions of the PUs' activity pattern, assumed to be completely unknown. In the proposed model, each cognitive node may have its own individual view on each PU's distribution, and, hence, seeks to find partners having a correlated perception. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use a combination of Bayesian nonparametric models such as the Dirichlet process and statistical goodness of fit techniques in order to improve the accuracy of the estimated PUs' activity distributions. Simulation results show that the proposed algorithm significantly improves the estimates of the PUs' distributions and yields a performance advantage, in terms of reduction of the average achieved Kullback-Leibler distance between the real and the estimated distributions, reaching up to 36.5% relative the non-cooperative estimates. The results also show that the proposed algorithm enables the cognitive nodes to adapt their cooperative decisions when the actual PUs' distributions change due to, for example, PU mobility.Comment: IEEE Journal on Selected Areas in Communications (JSAC), to appear, 201